Oil and gas wells are typically stimulated by introducing a mixture of fluid and proppant into the well in order to create and/or propagate fractures. One common method of hydraulic fracturing or “fracking” is the “plug-and-perf” method.
The plug-and-perf process consists of pumping a plug and perforating guns to a given depth. The plug is set, the zone perforated, and the perforating guns removed from the well. A ball is pumped downhole to isolate the zones below the plug, and the fracture stimulation treatment is then pumped in. The ball-activated plug diverts fracture fluids through the perforations into the formation. After the stage is completed, the next plug and set of perforations are initiated, and the process is repeated moving further up the well.
Plug-and-perf completions are extremely flexible multistage well completion techniques for cased hole wells. Each stage can be perforated and treated optimally because options remain open and variations can be exercised up to the moment the perforating gun is fired. The engineer can apply knowledge from each previous stage to optimize treatment of the current stage.
Each stimulation stage targets multiple sets of perforations, or called “perf clusters,” in the well at one time. However, the distribution of the total flow into or out of each perf clusters is usually unknown, and that can lead to less efficient operation and increased cost.
Distributed Acoustic Sensing (DAS) is an acoustic detection technology that has recently been applied in production and geophysical settings. Downhole DAS is a fiber-optic distributed sensing technology that can provide key diagnostic insights during hydraulic fracturing operations.
In practice, fiber-optic cables can be installed in vertical and horizontal wells, which can be treatment wells, injector wells or observation wells. Within the cable, there are often both single mode fibers for DAS and multi-mode fibers for DTS. Multiple fibers within one cable can offer redundancy and the ability to interrogate with different instrumentation simultaneously.
DAS is the measure of Rayleigh scatter distributed along the fiber optic cable. A coherent laser pulse is sent along the optic fiber, and scattering sites within the fiber cause the fiber to act as a distributed interferometer with a gauge length approximately equal to the pulse length. The intensity of the reflected light is measured as a function of time after transmission of the laser pulse. When the pulse has had time to travel the full length of the fiber and back, the next laser pulse can be sent along the fiber. Changes in the reflected intensity of successive pulses from the same region of fiber are caused by changes in the optical path length of that section of fiber. This type of system is very sensitive to both strain and temperature variations of the fiber and measurements can be made almost simultaneously at all sections of the fiber.
Raw DAS data are usually in the form of optical phase, with a range from −pi to +pi. The optical phase is defined by the interference pattern of the back-scattered laser energy at two locations separated by a certain length (gauge length) along the fiber. The phase varies linearly with a small length change between these two locations, which can be interpreted as axial strain change of the fiber in between. Depending on the vender, the measured optical phase is sometimes differentiated in time before it is stored. In this case, the DAS data can be considered as linear scaled fiber strain rates.
DAS has been used to monitor hydraulic fracturing operations. The applications include injection fluid allocation (e.g. Boone et al. 2015), hydraulic fracture detection (e.g. Webster et al. 2013), and production allocation (e.g. Palej a et al. 2015). DAS has been also used extensively to measure strain in hydrocarbon wells. Hill (U.S. Pat. No. 8,950,482) monitors hydraulic fracturing during oil/gas well formation. Tubel (US20060272809) controls production operations using fiber optic devices. Hartog (US20090114386) deploys an optical fiber as a distributed interferometer that may be used to monitor the conduit, wellbore or reservoir. Minchau (US20130298665) provides an in-situ permanent method for measuring formation strain in a volume around a treatment well. In McEwen-King (US20130233537), acoustic data from distributed acoustic sensing is processed together with flow properties data to provide an indication of at least one fracture characteristic. This is in no way an all-encompassing review of the technology. A recent review was published by Webster (2013) and the field has continued to advance rapidly.
Unfortunately, a common problem in optimizing the performance of horizontal wells stimulated via hydraulic fracturing is determining the relative amounts that each fracture stage is contributing to the total oil production. Without this information, it is difficult to assess the effectiveness of various well treatment strategies during completion, or after production has commenced.
It has been proposed to use non-intensity based methods of using DAS for fluid distribution, which calculate a fluid velocity by having the DAS track a moving harmonic or disturbance of flow across the injected section. Also, some of the concepts could apply to methods of using a noise logging tool instead of DAS data. Examples of this are shown in McKinley, et al. (1973) and McKinley & Bower (1979).
WO2016069322 describes a method of treatment design to determine if the perforations are sealed, where the flow rate is modeled by FPM (Fracture Plugging Model) based on the principle of mass conservation and momentum conservation. However, without closely monitoring each perforation cluster, the model can only determine the overall flow rate, and cannot predict fluid/proppant behavior at a specific perforation cluster.
U.S. Pat. No. 9,416,644 describes a method of characterizing a downhole hydraulic fracturing process, in which flow data acquired by a flow monitor is correlated with the acoustic data acquired from DAS to obtain characteristics of the fracturing process. The flow monitor itself is an additional cost and may not be suitable for all fracturing operations.
Therefore, there is a need for an efficient way of measuring DAS data and converting the DAS noise intensity to the flow rate for each perforation cluster so as to calculate the optimal completion design.